Techniques for linking data to provide improved searching capabilities

ABSTRACT

A machine-learning model may be previously trained with a supervised learning algorithm to identify whether a pair of labels provided as input are similar. A locality sensitive hashing forest (LSH) may be generated for the set of candidate labels. When a user later identifies an input label (e.g., by search query, by interface selection, etc.) the input label may be used to query the LSH forest to identify a subset of the candidate labels. This subset may be used to generate respective pairs comprising the input label, one of the subset candidate labels, and a corresponding feature set generated for the pair. This data may be provided to the model to identify a degree to which the pair of labels are similar. The user may be provided one or more recommendations including similar terms identified from the model&#39;s output.

BACKGROUND

Organizations use cloud computing environments to produce data using avariety of data sources and data services such as data lakes, datawarehouses, analytics, and the like. The variety and amount of this datamakes it difficult for data producers such as data engineers, datastewards, data scientists, and the like to find trusted data andunderstand that data to improve data governance. Discovering andmanaging this data is beneficial for various tasks. Some cloud computingsystems include a data management service that enables users todiscover, organize, enrich, and trace organization's technical metadataand data assets. The user may utilize the service to enrich knowledge ofavailable data by classifying data entities (e.g., a database table orview, logical files, etc.) and attributes according to domaindefinitions. Organizing data assets based on such definitions providesdeep insights when compared to the characteristics of technical metadata(e.g., based on storage names, column names, etc.). Today, such adiscovery based on domain definitions, requires manual linking of domaindefinition to the technical metadata. The user can manually add dataassets and entities and annotate these objects to improve productivity.Manual search and linking is an onerous and time-consuming task and tendnot to be scalable when the volume of metadata grows over a period.

BRIEF SUMMARY

Techniques are provided (e.g., a method, a system, non-transitorycomputer-readable medium storing code or instructions executable by oneor more processors) for provisioning resources of a cloud-computingenvironment to a user based at least in part on user-definedconstraints. Various embodiments are described herein, includingmethods, systems, non-transitory computer-readable storage media storingprograms, code, or instructions executable by one or more processors,and the like.

One embodiment is directed to a method for recommending one or moresimilar labels for a label provided as input. The method may includeobtaining, by a computing device, a machine-learning model that isconfigured to identify a similarity between a first label and a secondlabel provided as input. In some embodiments, the machine-learning modelhas been previously trained based at least in part on a supervisedlearning algorithm and a training data set including pairs of labelsthat have been previously identified as being valid or invalid pairs(e.g., similar, or disparate). In some embodiments, the first labelcorresponds to a technical label for a data entity and the second labelcorresponds to a domain term. Similar techniques may be employed using adomain term as the first label and the technical label as the secondlabel. The method may further include generating a locality sensitivehashing forest based at least in part on one or more respective n-gramtokens generated from respective domain labels of a plurality of domainlabels (e.g., a predefined glossary). The method may further includereceiving, from a user interface, user input identifying an input label.The method may further include identifying a plurality of candidatelabels from the locality sensitive hashing forest based at least in parton querying the locality sensitive hashing forest using one or moren-gram tokens generated from the input label. In some embodiments, theplurality of candidate labels comprises a subset of domain labels fromthe plurality of domain labels. The method may further includegenerating a respective pairing for the input label and each candidatelabel of the plurality of candidate labels. The method may furtherinclude generating a respective feature set for each respective pairingof the input label and each candidate label. The method may furtherinclude providing each respective feature set to the machine-learningmodel as input. The method may further include receiving output from themachine-learning model identifying one or more of the candidate labelsas one or more recommended labels. In some embodiments, the one or morerecommended labels are identified by the output as being similar to theinput term. The method may further include providing, via the userinterface, one or more recommendations corresponding to the one or morerecommended terms identified by the machine-learning model.

Another embodiment is directed to a data management service comprisingone or more processors and one or more non-transitory computer-readableinstructions that, when executed by one or more processors of acomputing device, cause the computing device to perform the disclosedmethods.

Yet another embodiment is directed to a non-transitory computer-readablestorage medium storing computer-executable instructions that, whenexecuted by one or more processors of a computing device, cause thecomputing device to perform the disclosed methods.

The foregoing, together with other features and embodiments will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an example flow for identifying a recommended labelfor an input term, in accordance with at least one embodiment.

FIG. 2 illustrates an example method for training and/or updating amachine-learning model, in accordance with at least one embodiment.

FIG. 3 illustrates an example training data set, in accordance with atleast one embodiment.

FIG. 4 illustrates a method for generating a feature vectorcorresponding to a pair of terms, in accordance with at least oneembodiment.

FIG. 5 illustrates an example method for building a Locality SensitiveHashing (LSH) Forest, in accordance with at least one embodiment.

FIG. 6 illustrates an example method for generating a recommendationusing a machine-learning model and an LSH forest, in accordance with atleast one embodiment.

FIG. 7 is a block diagram illustrating an example method for identifyingone or more recommended labels for an input label, in accordance with atleast one embodiment.

FIG. 8 is a block diagram illustrating a pattern for implementing acloud infrastructure as a service system, according to at least oneembodiment.

FIG. 9 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 10 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 11 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 12 is a block diagram illustrating an example computer system,according to at least one embodiment.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofcertain embodiments. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive. The word “exemplary”is used herein to mean “serving as an example, instance, orillustration.” Any embodiment or design described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother embodiments or designs.

The present disclosure relates to techniques for linking data to provideimproved searching capabilities. These improved searching capabilitiesmay be realized in many contexts. By way of example, a cloud-computingdata catalog (referred to as a “data catalog” for brevity) may beconfigured to include a glossary (e.g., a set of domain terms for whichdefinitions are known to one trained in the given domain such as medicalterms, marketing terms, etc.) and a set of technical metadata (e.g., oneor more terms and/or labels associated with a data entity such as afile, an object, a container, a data structure, a portion of anobject/container/data structure (e.g., an attribute, a column, a row ofa database, a data field, etc.) associated with a tenant. The terms“term” and “label” may be used synonymously herein. The cloud-computingdata catalog may be managed by a data management service of acloud-computing environment.

In some embodiments, technical metadata (e.g., a technical term) used tolabel one or more technical data entities may vary widely from thedomain terms that are known and used by practitioners within the domain.For example, a database or column of a database may be labeled“CUST_ADDS.” A user that is knowledgeable in the given domain mayrecognize this label as indicating a technical label for a data entity(e.g., the database or column) that contains customer addresses.However, were the user to search the data catalog using the more widelyused domain term, such as “customer address,” or “customer addresses,”the data catalog would not automatically identify the database or columnas being related to the provided search terms. Conventionally, a usermay manually link (e.g., associate) the technical metadata (e.g., thetechnical label “CUST_ADDS”) with a particular domain label (e.g.,“customer address”). Once linked, a subsequent search of the datacatalog for the particular domain label (e.g., “customer address”) wouldthen result in a set of search results that would include the technicalmetadata associated with that domain label such as “CUST_ADDS” from thetechnical metadata manually linked to the domain label by the user.However, when the data catalog includes tens, hundreds, thousands, ormore instances of technical metadata and/or domain terms, the manualprocess for establishing relationships between technical labels used fordata entities to domain terms can be an arduous task that requires vastamount of manpower and time as well as specialized knowledge.Improvements to identifying these label associations would be beneficialin more efficiently improving the data catalog's ability to identifyrelated data entities during search or at any suitable time (e.g., atstartup, periodically, or the like).

In some embodiments, a locality sensitive hashing (LSH) forest may begenerated for a collection of domain terms (e.g., one or more predefinedglossaries provided by the data management service). These domain termsmay be part of a predefined glossary that identifies the domain termsand, in some embodiments, the definitions and/or meanings correspondingto those domain terms. In some embodiments, a number of n-gram tokensmay be generated for each domain term. The characters within each token(e.g., the “n” in n-gram) may be predefined or user configurable. Insome embodiments, a minimum-hash matrix may be generated for some numberof permutations of the n-gram token for a given term. A number oflocality sensitive hashing trees may be generated from the n-gram tokenssuch that the minimum-hash matrices of the n-gram token are representedas paths within a tree of the LSH forest. To generate the LSH forest, aset of n-grams including various permutations of those n-grams arehashed with a locality sensitive hashing function (also referred to as a“hashing function,” for brevity). The hashing function may be configuredsuch that similar objects (e.g., similar sets of n-gram tokens) are muchmore likely to collide (e.g., hash to a same “bucket,” in this example,a tree) than dissimilar n-gram token sets. Each LSH tree, by virtue ofthe hashing algorithm used to hash the n-grams, may include a set ofdomain terms that are similar to one another to some degree. Someexamples of similarity measurements used in such LSH forests may includea Jaccard coefficient, a Hamming metric, and the like. It should beappreciated that similar techniques may be used to generate an LSHforest of technical labels such that recommendations can be provided ina bi-directional manner (e.g., from domain label to technical label, andvice versa).

As another preprocessing step, a machine-learning model may be trainedto identify a similarity score quantifying a degree of similaritybetween two terms (e.g., a label associated with a technical/data entityand a domain term). The machine-learning model may be trained utilizingany suitable supervised machine-learning algorithm and a labeledtraining data set. In some embodiments, the labeled training data setmay include pairs of terms for which a degree of similarity has alreadybeen assessed. In some embodiments, the degree of similarity of thetraining data set may indicate that the pair of terms are similar (e.g.,indicated with value 1), or disparate (e.g., indicated with a value of0). In some embodiments, a feature set may be generated for each term ofa pair and these feature sets may be used to train the model. Thegeneration of the feature set is elaborated in more detail below withrespect to FIG. 4 . In some embodiments, the respective feature set foreach term may be included in the training data set. Once trained, theaccuracy of the machine-learning model may be assessed. In someembodiments, a portion (e.g., 60%) of the training data set may beutilized to train the machine-learning model. Another portion (e.g.,20%) of the training data set may be utilized to tune the model. Someportion of the training data set (e.g., a remaining 20%) may be utilizedas a test set to assess the model's accuracy (e.g., an amount/percentageby which the output of the model agrees or disagrees with the pairingsof the remaining portion of the training data set).

At run time, a user may browse within a data catalog maintained by adata management service. The data catalog may include user interfacesfor accessing glossary terms (also referred to herein as “domain terms,”and technical metadata (e.g., data that identifies aspects of atechnical entity such as a file, database, object, structure, or thelike, and any suitable portion of the same). When the user navigates toa network page corresponding to a domain term, a number ofrecommendations corresponding to labels associated with respectivetechnical entities can be presented at the user interface. Likewise,should the user navigate to a network page associated with a data entity(e.g., a row of a database), the user may be presented with a number ofrecommended domain terms. The user can provide user input (e.g., via auser device operated by the user) indicating that a number of selectedterms are to be associated with the term corresponding to the networkpage from which the user input was provided. The association between thetechnical metadata (e.g., a technical label) and the domain label may bepersisted. Subsequently, a search query may be received that is similarto some degree to the domain label. In response to the search query, theassociated technical label(s) may be identified and the technicalmetadata corresponding to those entities may be returned as part of theset of search results obtained using the query.

In some embodiments, an association between technical labels and domainlabels may occur in an automated fashion and not necessarily triggeredby user input. For example, at startup, or at another suitable time, theservice may attempt to identify one or more technical labels for eachdomain label, or vice versa, using similar techniques as discussedabove. In some embodiments, an association may be automaticallygenerated when the output of the model indicates a degree of similaritybetween the two input terms that exceeds some predefined threshold.

The techniques discussed herein provide for an improved user experienceas users need not be sophisticated or particularly knowledgeable. Withthese associations in place, technical entities may be identified bytheir corresponding domain terms, and vice versa. This process may beperformed at any suitable time (e.g., on startup, as part of apreprocessing step performed before runtime). In some embodiments, arecommendation can be provided to the user before an association ismade. In other embodiments, if confidence is high enough that the pairof technical and domain terms are similar (e.g., having over a thresholdamount of similarity), an association may automatically be generatedbetween the two. These techniques can save significant time and effortof the data owners and data administrators. Additionally, a user neednot have prior knowledge and/or training setting up these types ofassociations.

Moving on to FIG. 1 , which illustrates an example flow 100 foridentifying a recommended term for an input term, in accordance with atleast one embodiment. By way of example, an input term may be a domainterm that is part of a predefined glossary and the recommendation labelsought may correspond to a technical term that is deemed to be similarto the domain term. Conversely, the input term may be a technical term(e.g., a technical term associated with one or more data entities suchas a database, file, container, data object, or any suitable portion ofthe same), and the recommended label may correspond to a domain termfrom the predefined glossary. The operations discussed with respect toFIG. 1 (and expanded upon in connection with FIGS. 2-7 ) may be executedby a recommendation engine. In some embodiments, this recommendationengine may operate as part of a data management service 103. Datamanagement service 103 may be configured to manage a data catalogincluding any suitable number of domain terms corresponding to anysuitable number of predefined domain term sets (e.g., glossaries) andany suitable number of technical terms corresponding to one or more dataentities (e.g., a database, a file, a container, a data object, etc., orany suitable portion of the same). The data management service 103 mayoperate as a component of the cloud-computing patterns described belowwith respect to FIGS. 8-12 .

The flow 100 may begin at 102, where a machine-learning model may betrained to indicate the similarity between a pair of input terms. Themachine-learning model (e.g., model 104) may be trained utilizing anysuitable supervised learning algorithm and training data 106. Eachexample of the training data may include two terms (e.g., a domain termand a technical term), a feature set generated from the two terms, and alabel indicating a degree to which the two terms are similar. An exampletraining data set is discussed in more detail with FIG. 3 . Generationof a feature set is discussed in more detail with respect to FIG. 4 .The training process itself is elaborated on more fully with respect toFIG. 2 . Through this training process, the model 104 may be configuredto provide output 108. Output 108 may provide a score indicating adegree to which the pair of terms were determined to be similar. In someembodiments, output 108 may include a confidence score that indicate adegree of confidence associated with a determination that the two inputterms are similar although other indications identifying a degree ofsimilarity between the two input terms are contemplated. For example,the output 108 could include a score quantifying a degree of similarity(e.g., 0.8 indicating 80% similarity, etc.) or the like.

At 110, user input may be received identifying a source object. By wayof example, a user interface may be provided (not depicted) thatprovides an option to select an option associated with a source object(e.g., a term for which a similar term is to be sought). As anon-limiting example, the source object could be a technical termassociated with one or more data entities. For example, the user mayselect a link associated with an attribute of a database (e.g., a columnof a database) labeled “PROD_DSC” as depicted at 112. The selection ofthis link may be considered user input identifying the label/term“PROD_DSC” as the source object.

At 114, a number of target objects may be identified as having somesimilarity to the source object. By way of example, a set of targetobjects may include domain terms from a glossary. These domain terms(e.g., “Product Description,” “Product Information,” “Product,” “Time,”“Price,” “Payment,” etc.) may correspond to a definition within theglossary. These domain terms may be terms that are regularly used andunderstood by those practicing within the given domain (e.g., a medicalfield, a marketing field, a business, etc.). The particular termsincluded in such a glossary may depend on the context and domainutilized. To identify the number of target objects (e.g., one or moredomain terms having some threshold degree of similarity) a number ofcandidate pairs (e.g., one or more pairs including the target object andone of the domain terms) may be provided as input to the model 202. Insome embodiments, the candidate pairs may not correspond to every knowndomain term. Rather, a subset of the domain terms (e.g., a candidate setof domain terms) may be identified. Techniques for identifying such asubset are discussed in more detail with respect to FIGS. 5 and 6 . As anon-limiting example, domain terms corresponding to recommendations 116,118, and 120 may be selected as the domain terms being most-similar tothe source object (e.g., technical term “PROD_DSC”). In someembodiments, multiple glossaries may be used and in these examples, someof the recommendations may correspond to different glossaries. Thus, insome embodiments, a recommendation can include additional metadata suchas a path indicating a source of the recommended term (e.g., glossary“xyz”, glossary “Glossary1,” etc.). In some embodiments, the additionalmetadata associated with the recommendation can indicate whether therecommended term has previously been accepted (e.g., by the user orother users) as being similar to the source object (e.g., “PROD_DSC”).

At 122, subsequent user input may be received indicating acceptance (orrejection) of one of the recommended terms. At 124, if the user inputindicates acceptance, the accepted term may be associated with thesource object and an association between the two terms (e.g.,association 126) may be stored for subsequent use.

At 128, the association 126 may be utilized to provide output to asubsequent query. By way of example, a user may enter in a query“Product Description” (e.g., Query 130) in a search interface. A searchalgorithm may be executed to identify corresponding terms and/or dataentities that correspond to the query. A set of associations (e.g.,including the association 126) may be utilized to identify correspondingterms and/or data entities. Thus, given a domain term “ProductDescription” as a query, the technical term “PROD_DSC” may be includedas part of the search results 132 provided in response to the query.These techniques enable the user to search for data entities (e.g., dataentities corresponding to a technical term) by a domain term (e.g., amore commonly used/known term in the field in which this recommendationis provided) rather than having to have knowledge of the actual name ofthe data entity (e.g., “PROD_DSC”) which may be obscure or difficult toremember.

Similar techniques may be employed to enable the user to select a domainterm from a glossary and be presented with a set of technical termscorresponding to data entities within the system. As yet anotherexample, the techniques discussed herein and with respect to FIG. 1 ,may be utilized to enable the user to select a data entity correspondingto a technical term (e.g., access a network page associated with a database column “PROD_DSC”).

FIG. 2 illustrates an example method 200 for training and/or updating amachine-learning model, in accordance with at least one embodiment. Insome embodiments, the method 200 may be performed by a data managementservice (e.g., the data management service 103 of FIG. 1 ). The datamanagement service may operate as any suitable part of thecloud-computing patterns of FIGS. 8-12 . In some embodiments, the method200 may be performed by a computing component different from the datamanagement service and the machine-learning model may be accessible tothe data management service.

The method 200 may begin at 202, where a determination may be made as towhether a given model has yet to be trained (e.g., is this a firstinstance of training for this model?). If so, the method 200 may proceedto 204. If it is determined that the model has already previously beentrained, then the training data is to be used to update thepreviously-trained model and the method 200 may proceed to 216.

When initially training the model, a baseline training data set may beobtained at 204. This training data set may be obtained frompredetermined and obtained from storage. FIG. 3 illustrates an exampletraining data set 300, in accordance with at least one embodiment. Insome embodiments, the training data set 300 may be generated by a datamanagement service (e.g., the data management service 103 of FIG. 1 ).In some embodiments, the training data set 300 may be generated by acomputing component different from the data management service andaccessible to the data management service.

Training data set 300 may include pairings (also called associations)between two terms/labels. By way of example, column 302 may include anysuitable number of technical terms/labels (e.g., one or more technicallabels that are individually associated with one or more technical dataentities such as a database, object, container, file, or any suitableportion thereof). Column 304 may include any suitable number of domainterms/labels which individually correspond to the technical terms/labelsof column 302. Column 306 may include similarity values that indicate adegree of similarity between a pair of terms/labels (e.g., between apair of labels of a given row). Although the similarity value isexpressed here as a label itself (e.g., similar/disparate), thesimilarity value may be expressed as a Boolean value, as an integer(e.g., 1=Similar, 0=Disparate), as a percentage (e.g., 0.65 similar,equivalent to 0.35 disparate), or any suitable measurement and/orindication of similarity and/or disparity. For simplicity, the trainingdata set 300 is not depicted as including a feature set associated witheach label, but it should be understood that a training data set used toin a process for training may additionally include a feature set ofcorresponding terms/labels in a given pair (e.g., a row). FIGS. 2 and 4further discuss the usage and content of some example feature sets.

Returning to FIG. 2 , once the baseline training data set (e.g.,training data set 300 of FIG. 3 ) is obtained, the method 200 mayproceed to 208, where a set of preprocessing steps may be executed. Insome embodiments, a first set of preprocessing steps may be performed onthe technical terms/labels of the training data set. By way of example,the first set of preprocessing steps may include trimming precedingand/or succeeding blank spaces if any exist in the technical term. Insome embodiments, the first set of preprocessing steps may includereplacing one or more hyphens (e.g., “-”) with an underscore character(e.g., “_”). In some embodiments, the first set of preprocessing stepsmay include splitting (e.g., tokenizing) the technical term at a “.” orat a “_ to obtain a list of tokens for the technical term. In someembodiments, the first set of preprocessing steps may include tokenizingeach token further using a regular expression (Regex) based tokenizeralgorithm which modifies the strings/tokens to include only alphabeticcharacters (e.g., by removing numerical and/or special characters). Insome embodiments, the first set of preprocessing steps may includeconverting uppercase letters to lowercase letters or vice versa. In someembodiments, the first set of preprocessing steps may include removingstop words (e.g., a, the, and) from the tokens. In some embodiments, thefirst set of preprocessing steps may include removal of any token of apre-specified length (e.g., tokens of length 1). In some embodiments,the first set of preprocessing steps may include any suitable predefinedrule (e.g., one or more rules that are predefined to remove tokeninconsistent cases). In some embodiments, the first set of preprocessingsteps may include accessing a predefined synonym list and identifyingsynonyms for a token from a predefined association within the list, thenadding the synonyms to the set of tokens. In some embodiments, the firstset of preprocessing steps may include assigning a final list of tokensobtained using any suitable combination of the preprocessing steps aboveas a final list of tokens corresponding to a given technical term. As anon-limiting example, using the preprocessing steps above, a technicalterm such as “CUST_ADD123_ID4” may be tokenized to correspond to a tokenset of [“cust”, “add”, and “id”].

Similarly, a second set of preprocessing steps may be executed againsteach of the domain terms of the training data set. By way of example,the particular set of preprocessing steps executed against each domainterm may include any suitable combination of the first preprocessingsteps listed above. For example, each domain term may be modified toremove preceding and/or succeeding blank spaces. In some embodiments,the second set of preprocessing steps may include tokenizing each tokenusing a Regex based tokenizer to modify the strings (e.g., the stringsobtained from removing the blank spaces) to include only alphabeticcharacters. In some embodiments, the second set of preprocessing stepsmay include one or more operations that may be performed to removeinconsistent token cases. In some embodiments, the second set ofpreprocessing steps may include converting the token characters tolowercase if needed. In some embodiments, the second set ofpreprocessing steps may include removing any token of having a givenlength or length(s) (e.g., removing tokens of length 1, 2, etc.). Insome embodiments, the second set of preprocessing steps may includeassigning a final list of tokens obtained from the domain term using anysuitable combination of the preprocessing steps above as a final list oftokens corresponding to a given domain term. As a non-limiting example,using the preprocessing steps above, a technical term such as “CustomerAddress” may be tokenized to correspond to a token set of [“customer”,“address”].

The method 200 may proceed from 208 to 210, where one or more featuresets (also referred to as a “feature vector”) may be generated. In someembodiments, a feature set may be generated for a pair of terms (e.g., apair including a technical term and a domain term corresponding to row308 of the training data set 300 of FIG. 3 ).

FIG. 4 illustrates a method 400 for generating a feature vector 402corresponding to a pair of terms, in accordance with at least oneembodiment. The method 400 may be performed by the data managementservice 103 of FIG. 1 or any suitable computing component of thecloud-computing patters of FIGS. 8-12 .

The method 400 may begin at 404 where the technical term and domain termmay be tokenized. Tokenizing the technical term and the domain term mayutilize any suitable combination (e.g., a predefined combination) of thepreprocessing steps described above in connection with 208 of FIG. 2 .

At 406, one or more scores can be computed for the pair (andpotentially, each pair in the training data set 300). In someembodiments, the scores computed may be one or more similarity scoresthat are generated using any suitable number of similarity assessmentalgorithms. A similarity assessment algorithm may be configured toassess an amount of similarity and/or disparity between input dataincluding a pair of terms. Some example similarity assessment algorithmsmay include, but are not limited to a Jaro Winkler Distance Algorithm, aLevenshtein Distance algorithm, a Burrows Wheeler Transform Algorithm,an Overlap Algorithm (e.g., that identifies an amount of overlap betweentwo input terms), and a prefix algorithm (e.g., an algorithm forcomparing prefixes of two input terms for similarity/disparity).

In some embodiments, at least one of the scores may be computed. Forexample, a “left score” may be computed by calculating a first ratio ofthe unique number of intersected n-gram token between the two strings(e.g., the first token of one term (e.g., the technical term) beingreferred to as “token 1,” and a first token of the other term (e.g., thedomain term) being referred to as “token 2”) to the number of n-grams intoken 1. In some embodiments, a “right score” may be computed bycalculating a ratio of unique intersected n-grams between two givenstrings to the unique number of n-grams in token 2. An “intersectionover union score” can be computed by calculating a ratio of the uniquenumber of intersected n-grams to the number of unique n-grams of the twogiven strings.

As a non-limiting example, consider the terms of row 308 of FIG. 3 . Forthe source object (e.g., the technical term), “CUST_ADD123_ID4” may betokenized according to the preprocessing steps discussed above inconnection with 208 of FIG. 2 to generate a token set of [“cust”, “add”,“id” ]. Similarly, the target object (e.g., the domain term, the termfor which similar terms are sought) may be tokenized according to thepreprocessing steps discussed at 208 in connection with domain terms togenerate a token set of [“customer,” “address” ]. For each source objecttoken (i.e., “cust”, “add”, and “id”) the following algorithm may beapplied. First, the algorithm may include computing a score between“cust” (e.g., token 1 of the source object) and “customer (e.g., token 1of the target object) using a set of similarity algorithms 408. By wayof example, if similarity algorithms 408 included all eight similarityalgorithms discussed above, eights scores would be calculated, eachquantifying a degree of similarity between “cust” and “customer” basedon a corresponding similarity algorithm of the set. An example set ofscores is depicted in row 410 (e.g., [0.5, 0.3, 0.4, 0.6, 0.77, 0.23,0.67, 0.89]). An overall score (e.g., 4.36) can be computed for thesimilarity of “cust” and “customer” by summing the scores of row 410.Next, the algorithm may include computing scores between “cust” (e.g.,token 1 of the source object) and “address” (e.g., token 2 of the targetobject) (corresponding to the set of scores of row 412 [0.2, 0.22, 0.19,0.11, 0.1, 0.28, 0.23, 0.2). An overall score (e.g., 1.53) can becomputed quantifying the similarity of “cust” and “address” by summingthe scores of row 412. The same techniques can be applied to calculate aset of scores and an overall score for the comparisons identified forrows 414-420.

At 422, a feature vector (e.g., the score set corresponding to column424 of a given row) with the highest overall score may be selected foreach token of the technical term. For example, row 410 and row 412 mayinclude overall scores for comparing “cust” (e.g., token 1 of the sourceterm) to each of the target object's tokens (e.g., “customer” beingtoken 1 of the target object, and “address” being token 2 of the targetobject). In this example, the vector of row 410, column 424 (e.g., [0.5,0.3, 0.4, 0.6, 0.77, 0.23, 0.67, 0.89]) corresponding to the comparisonof “cust” to “customer” is selected based on determining that theoverall score for that comparison (e.g., 4.36) is higher than all theother overall scores of other comparisons that use the same source token(e.g., in this example, 1.53 corresponding to row 412). Similarly, ahighest overall score for rows 414 and 416 may be utilized to determinea vector for comparisons that utilize the second token of the sourceobject (e.g., “add”). In this example, the vector of row 416 may beselected based on determining the overall score for row 416 (e.g., 4.81)is higher than the overall score for row 414 (e.g., 0.964). A highestoverall score for rows 418 and 420 may be utilized to determine a vectorfor comparisons that utilize the third token of the source object (e.g.,“id”). In this example, the vector of row 420 may be selected based ondetermining the overall score for row 420 (e.g., 0.89) is higher thanthe overall score for row 418 (e.g., 0.82).

At 426, a final feature vector may be computed. In some embodiments, thefinal feature vector may be computed based on the feature vectorsselected at 422. By way of example, the sum of each value of a featurevector may be summed with the other corresponding values of theremaining feature vectors and the sum may be divided by the number offeature vectors. In the ongoing example using three vectors, thecomputation can be expressed:

-   -   Vector 1: [a1, b1, c1, d1, e1, f1, g1, h1]    -   Vector 2: [a2, b2, c2, d2, e2, f2, g2, h2]    -   Vector 3: [a3, b3, c3, d3, e3, f3, g3, h3]        Final Feature Vector:        [(a1+a2+a3)/3,(b1+b2+b3)/3,(c3+c2+c3)/3,(d1+d2+d3)/3,(e1+e2+e3)/3,(f1+f2+f3)/3,(g1+g2+g3)/3,(h1+h2+h3)/3]        The computation using the vectors of rows 410, 416, and 420 is        depicted at 428 and the final feature vector is depicted at 430.        In some embodiments, any suitable combination of the final        feature vector, the highest scored feature vectors (in this        example the vectors corresponding to rows 410, 416, and 420), or        the vectors computed for each comparison (e.g., the vectors        corresponding to row 410-420) may be included as part of the        training data set (e.g., in this example, as part of row 308) of        FIG.

Returning to FIG. 2 , once the final feature vector for each pair ofterms has been computed it can be added to the training data set examplesuch that each entry of the training data set includes the technicalterm, the domain term, the final feature vector, and a label indicatingthat the pair is similar (e.g., an acceptable match) or disparate (e.g.,an unacceptable match).

At 212, one or more machine-learning models may be generated using thetraining data set as augmented with the final feature vectors calculatedin accordance with FIG. 4 . In some embodiments, each machine-learningmodel may be trained utilizing a unique supervised learning algorithm.By way of example only, in some embodiments four models may be created,each model being trained utilizing a logistic regression algorithm, asupport vector machine algorithm, a random forest algorithm, and atwo-layered neural network, respectively. Each model may be configuredin this manner to identify a score (e.g., a confidence score thatindicates a degree of confidence that the two input terms have over athreshold degree of similarity). As another example, the models may beconfigured to identify a score that quantifies a degree of similaritybetween the two input terms. It should be appreciated that othersupervised learning algorithms may be utilized, and the particularsupervised learning algorithm are utilized in this example forillustrative purposes only. The specific number of models trained may begreater or less than four, with the specific number and/or algorithmsused being predefined or user configurable.

In some embodiments, some portion of the training data set (e.g., 60%,50%, etc.) may be utilized to train the model(s), another portion of thetraining data set (e.g., 20%) may be used to tune the model, and theremaining portion not used for training (e.g., 20%, 30%, etc.) may beutilized to assess the accuracy of each model. For example, each of theremaining examples of the training data set that were not previouslyused for training, may be provided as input to the machine-learningmodel. The subsequent output from the machine-learning model may beutilized to determine whether the input terms are similar (e.g., havinga confidence score over a threshold indicating similarity, having asimilarity score that meets or exceeds a predefined similaritythreshold, etc.) or disparate (e.g., having a confidence score under athreshold indicating disparity, having a similarity score that is undera predefined similarity threshold, etc.). Each model may be scored basedon a ratio between the number of times the model provided output thatagreed with the label of the training data set example over the totalnumber of training data examples provided for the assessment. A modelhaving the highest accuracy score may be selected for subsequent use. Insome embodiments, each of the models may be stored in data store 214 andupdated at any suitable time.

At runtime, a user may provide feedback indicating whether two termsdeemed to be similar by the model are in fact similar. In someembodiments, the user may be provided a recommendation that recommendsassociated a domain term with a technical term identified by the modelas being the most similar (e.g., having a highest value output) of anumber of candidate terms. If the user accepts the recommendation (e.g.,via user input such as selecting an “accept” button associated with therecommendation), the feedback may be utilized to identify a new trainingexample that includes the pair of terms, their corresponding featurevectors, and an indication that the pair of terms are similar, or inother words, a good match. If the user explicitly rejects therecommendation or does not accept the recommendation within a predefinedthreshold, a new training data example may be added to the training dataset that includes the pair of terms, their corresponding featurevectors, and an indication that the pair of terms are disparate, or inother words, mismatched.

In some embodiments, a data management service may utilize the model toidentify a similar term for a set of input terms. As non-limitingexample, the data management service may take a set of domain terms(e.g., a glossary) and identify one or more similar technical termscorresponding to a data entity using by calculating a feature vector foreach unique domain term/technical term (DT/TT) pairing. In someembodiments, if the output of the model indicates the pair of terms aresimilar (e.g., a good match), the service may be configured toautomatically generate and store an association between the two termswithout needing advance user input. The user may be provided a list ofthese automatically generated associations and an interface to acceptand/or reject the associations. If accepted or rejected, a new trainingdata set example may be added corresponding to the terms of the acceptedor rejected association that indicates the terms are similar/a match(for accepted associations) or disparate/mismatched (for rejectedassociations).

Additional training data examples generated based on user feedback maybe referred to as “curated user feedback.” The curated user feedback maybe stored in any suitable storage location.

At any suitable time, process 200 may be repeated. However, in theongoing example, the next time the process 200 is executed, thedetermination made at 202 may indicate that training has alreadyoccurred and thus the process may proceed to 216 where the curatedtraining data (e.g., training data examples generated based on userfeedback at run time) may be obtained. In some embodiments, the curatedtraining data may include new training data examples only and mayexclude training data examples that have been previously used to trainor update the model(s).

At 218, the same types of preprocessing steps discussed at 208 may beperformed for these curated training data examples. At 220, a featureset may be generated for each curated training data example in themanner discussed above at 210. At 222, the one or more models may beobtained from data store 214 and updated using the curated training dataexamples (now including corresponding the corresponding feature setsgenerated at 220). In this manner, user feedback may be utilized overtime to improve the accuracy of the models. At any suitable time, theaccuracy of the models may be re-evaluated using any suitable number oftraining data examples and a most-accurate model may be utilized forsubsequent recommendations and/or the automatic generation ofassociations between a domain term and technical term.

FIG. 5 illustrates an example method 500 for building a LocalitySensitive Hashing (LSH) Forest, in accordance with at least oneembodiment. In some embodiments, the method 500 may be considered apreprocessing step executed before runtime. The method 500 may beperformed by the data management service 103 of FIG. 1 or any suitablecomputing component of the cloud-computing patterns of FIGS. 8-12 .Thus, the LSH forest generated using the operations described withrespect to FIG. 5 may be generated by the data management service 103 ora different computing component but may be accessible to the datamanagement service 103.

The general objective of the methods described herein, is to identify amost-similar term given an input term (e.g., a technical term that ismost similar to a domain term, or vice versa). When the size of the dataset used is small (e.g., a small number of domain terms when the inputterm is a technical term, or vice versa), one could compare each object(e.g., each domain term) to the query (e.g., the input technical term)to find the most similar object. However, such an approach becomesinfeasible due to linear querying costs for larger data sets. The costof computing similarity between the input term and every possible objectcan overwhelm the processing resources of the system. In a scenario witha relatively large data set, it is beneficial to identify a subset ofcandidate objects that are most likely to be similar to the input term.The method 500 may employ Locality Sensitive Hashing (LSH) with aminimum hashing (min-hash) function to identify such a subset. It may beused, as described further in FIG. 6 , to reduce the dimensionality ofthe data set.

The method 500 may begin at 504 where a set of target object may beobtained. In this example, the set of target objects may be the set ofall domain terms, although in other examples, the method 500 may besimilarly executed to generate an LSH forest of technical terms. In someembodiments, two LSH forests may be generated, one for the set of allknown domain terms, and one for the set of all known technical terms.However, for illustration, this example will utilize the set of domainterms for the target objects. An example set of domain terms is providedin column 506 of table 508.

At 510, an LSH forest may be built. To build the LSH forest, the targetobjects may first be tokenized (e.g., utilizing the preprocessing stepsdiscussed above at 208 of FIG. 2 ). The corresponding set of tokensresulting from such tokenization is depicted for each target object incolumn 512 of table 508. Next, a set of n-gram tokens (also referred toas “n-grams”) may be generated for each token of column 512. Asdepicted, the n used is 3, resulting in n-gram tokens having a characterlength of 3. However, the particular n used for the n-grams (e.g., n=2,n=5, etc.) may be predefined and/or user configurable. Next, a minimumhashing algorithm may be executed to generate a min-hash matrix forvarious granularities (e.g., for each n-gram, for the n-grams of a giventoken, and/or for the set of all n-grams of all tokens of a given targetobject). The min-hash matrix for the n-gram “Pro” is depicted at 516.The min-hash matrix for the set of n-grams of token [“Pro”, “rod”,“odu”, “duc”, “uct” ] is depicted at 518. The min-hash matrix for theset of n-grams of all tokens in the domain term “Product Description”(e.g., Term [[“Pro”, “rod”, “odu”, “duc”, “uct” ], [“Des”, “esc”, “scr”,“cri”, “rip”, “ipt”, “pti”, “tio”, “ion” ]]) is depicted at 520. Thepurpose of the min-hash matrix is to reduce the dimensionality of then-gram sets, while maintaining the ability to assess the similaritybetween terms. The five target objects (e.g., domain terms) of table 508may be represented by min-hash matrix 522, where each column of themin-hash matrix 522 represents a min-hash of a single target object.

Once the min-hash matrices are generated, a LSH function can be executedon the min-hash matrices (rather than the n-gram sets themselves). TheLSH function may be configured to the min-hash matrices into buckets(e.g., each represented by a tree) such that similar min-hash matricesare hashed to the same bucket (e.g., tree), while dissimilar min-hashmatrices are hashed to disparate buckets (e.g., disparate trees). Thus,after executing the LSH function, the LSH forest may include anysuitable number of trees, each tree representing any suitable number ofsimilar terms (terms that were deemed to be similar by virtue of hashingto the same tree). The domain term “Product Description” is representedin the form of tree 524, where the bolded edges and corresponding nodesrepresent the min-hash matrix for that domain term.

Once generated, the LSH forest may be stored in any suitable storagelocation at 526.

FIG. 6 illustrates an example method 600 for generating a recommendationusing a machine-learning model (e.g., the machine-learning model trainedas described above in connection with FIGS. 2-4 ) and an LSH forest (theLSH forest generated in accordance with method 500 of FIG. 5 ), inaccordance with at least one embodiment.

The method 600 may begin at 602 where a user 604 may select a sourceobject. As an example, the source object may be a technical term and theuser 604 may access the source object via a user interface selection(e.g., by checking a box associated with the technical term, by typingin the technical term, by accessing a webpage associated with thetechnical term, or the like). As a non-limiting example, the user 604may access a network page associated with a technical term “PROD_DESC.”This technical term and/or network page may be associated with one ormore data entities (e.g., a database, a file, a container, a dataobject, or any suitable portion of the same). It should be appreciated,however, that the method 600 may be similarity employed to identify arecommendation for a domain term. In such examples, the source object(e.g., input term for which a recommendation is sought) may be a domainterm and the target objects (e.g., term from which a recommendation isselected) may be technical terms. In such examples, the LSH forestutilized may be one that includes the set of all technical terms (ratherthan the LSH forest generated from the domain terms utilized as targetobjects in FIG. 5 ).

At 606, the top N number of target objects may be identified. Byway ofexample, the source object (e.g., “PROD_DESC”) may be tokenized usingthe preprocessing steps described above in connection with FIG. 2 , togenerate a token set of [“prod”, “desc” ]. The tokens would be brokeninto n-grams (e.g., 3-gram) similar to the process discussed inconnection with FIG. 5 . The n-grams of the source object may be[[“Pro”, “rod” ], [“Des”, “esc” ]]. A minimum hash matrix may begenerated for each n-gram where subsequent n-grams would update themin-hash of tokens similar to that described at 516-520 of FIG. 5 . Themin-hash matrix for source object may be hashed using the same LSHhashing function utilized to generate the LSH Forest 608 (an example ofthe LSH forest generated using the method 500 of FIG. 5 ) and used toquery the LSH forest 608. The result of the query may identify aparticular tree of the LSH forest (or a top T number of LSH trees havingthe most similar min-hash matrices to the min-hash matrix of the sourceobject). The identified tree(s) may include a set of candidate targetobjects from which a recommended target object may be identified. Byutilizing the LSH forest, identifying a recommended target object neednot utilize the entire domain term set, but rather a smaller set ofcandidate domain terms that, by virtue of generating the min-hashmatrices and executing the LSH function, have already been determined tobe similar to the source object to some degree. As a non-limitingexample, the n-gram min-hash matrix for the example source object may bethe same as the one depicted at 520 and associated with the domain term“Product Description.” Thus, “Product Description” along with nearbysiblings (e.g., 2 nearby siblings such as “Product” and “ProductInformation”) in the forest as part of the top N (e.g., 3) termsidentified from the LSH forest 608. However, as Nis a number less thanthe number of target objects in the target object set, the potentialtarget candidate set has been pruned from the total number of targetobjects to N (in the ongoing example, from 5 to 3).

At 610, a determination may be made as to whether any target objectswere found by executing the operations discussed in connection with 606.If no target objects were found (e.g., the input term did not hash to atree that represents any other terms), the method 600 may proceed to 612where the user 604 is notified that no recommendation were found. Insome embodiments, the user is affirmatively notified (e.g., via textpresented at the network page associated with the input technical term)that no recommendations exist. In other embodiments, the notificationmay include simply not displaying any recommendations without explicitlynotifying the user that no recommendations were found.

If any target objects were found at 610, the method 600 may proceed to614 where a prediction data set may be generated. In some embodiments,generating a prediction data set may include generating a pairing, whereeach pair includes the source object (e.g., the technical term“PROD_DESC”) and one of the sets of target objects (e.g., one or moredomain terms) identified at 606 from the LSH forest 608.

At 616, a number of operations related to preprocessing and featuregeneration may be executed. These operations may correspond to theoperations discussed at 208 and 210 of FIG. 2 , and the method 400 ofFIG. 4 . In summary, the source object and target objects may betokenized and each unique pair of <source object, target object T>identified at 614, the corresponding tokens may be utilized with apredefined set of similarity algorithms to generate a feature set asdescribed in connection with FIG. 4 . The feature set corresponding to agiven pair may be added to the prediction data set. At 618, amachine-learning model (e.g., machine-learning model 620, an example ofthe machine-learning model trained in accordance with the method 200 ofFIG. 2 ), may be retrieved and used to identify one or morerecommendations for a target object (e.g., a domain term) that has beendetermined to be similar (to some degree) to the source object (e.g.,the technical term “PROD_DESC”). For example, each training data setexample of the prediction data set generated at 614 andprocessed/augmented at 616 may be utilized as input to the model. Theoutput of each example may include a confidence score that indicates adegree of confidence that the two terms match. For example, the outputof each input of the ongoing example may indicate the followingconfidence scores:

-   -   “PROD_DESC” and “Product Description”=0.89        -   “PROD_DESC” and “Product”=0.84    -   “PROD_DESC” and “Product Information”=0.78

At 622, one or more recommendation may be provided. In some examples, ahighest-scored pairing may be selected and the target object (e.g., inthis example, the domain term) may be provided as a recommendation. Forexample, the domain term “Product Description,” by virtue of having thehighest confidence score as provided by the machine-learning model 620,may be presented at the network page of the technical term.

At 624, the user may accept or reject the recommendation. Byway ofexample, user interface options may be provided (e.g., via the networkpage associated with the technical term) to accept or reject therecommendation. In some embodiments, accepting the recommendation mayinclude generating an association between the source term and therecommended target term and storing the association for future use. Whena subsequent query is received (e.g., a query for “ProductDescription”), the association may be utilized to return the associatedterm (e.g., the technical term “PROD_DESC” corresponding to one or moredata entities). Either accepting or rejecting the recommendation may beused as feedback to generate a new example for the training data set.For example, a new example for the training data set may include thesource object, the target object, the tokens/n-grams/feature setgenerated at 616, and a label or indicating whether the source objectand target object of the example were a good match (e.g., similar) whenthe recommendation was accepted or a bad match (e.g., disparate) whenthe recommendation was rejected. The examples may be stored in datastore 626 which may be the storage from which curated training data isobtained as described at 216 of FIG. 2 when the model(s) are to beupdated.

It should be appreciated that recommendations need not be initiated byuser input. For example, operations described at 602-618 may beperformed for all source objects in the system. That is, for example,all technical terms may be identified as source objects and theoperations of 602-618 may be performed for each technical term toidentify one or more recommended domain terms identified by the model asbeing the most similar to the technical term. The same process may beperformed to identify recommended technical terms for each domain termof a set of domain terms (e.g., domain terms of a predefined glossary).In some embodiments, the most similar term (or a predefined number ofhighest scored target terms) may be selected and an association betweenthe source object and the recommended target object may be automaticallygenerated. For example, in some embodiments, a threshold value may bepredefined (e.g., 0.8, 0.9, etc.) which defines a threshold confidencescore. If an output of the model corresponding to a source object/targetobject pair breached the threshold value (e.g., is greater than 0.8),then an association between the two may automatically be generated. Insome embodiments, a highest scored target object may be selected, and anassociation made between that target object (e.g., the recommended term)and the source object (e.g., the term for which a recommendation issought). In some embodiments, the score for the highest scored targetobject may also need to breach the predefined threshold value before anassociation will be automatically generated.

FIG. 7 is a block diagram illustrating an example method 700 foridentifying one or more recommended labels for an input label, inaccordance with at least one embodiment. The method 700 may beperformed, in whole or in part, by the data management service 103 ofFIG. 3 , operating as part of at least one of the cloud-computingpatterns of FIGS. 8-12 .

The method 700 may begin at 702, where a machine-learning model may beobtained, the machine-learning model (e.g., the machine-learning model202) being configured to identify a similarity between a first label anda second label provided as input. In some embodiments, themachine-learning model has been previously trained (e.g., utilizingmethod 200 described in connection with FIG. 2 ) based at least in parton a supervised learning algorithm and a training data set (e.g., thetraining data set 300 of FIG. 3 ). The training data set may includepairs of labels that have been previously identified as being valid orinvalid pairs. In some embodiments, the first label corresponds to atechnical term and the second label corresponding to a domain term.

At 704, a locality sensitive hashing (LSH) forest may be generated basedat least in part on one or more respective n-gram tokens generated fromrespective domain labels of a plurality of domain labels. By way ofexample, an LSH forest may be generated from a set of domain labels(e.g., the target objects of column 506 of FIG. 5 ). These domain labelsmay correspond to a predefined glossary. The LSH forest may be generatedusing the operations discussed in connection with FIG. 5 .

At 706, user input is received from a user device. The user input mayidentify an input label. In some embodiments, the user input may includea user interface selection such as, but not limited to, selecting alink, checkbox, option, or the like, that is associated with a technicalterm.

At 708, a plurality of candidate labels may be identified from thelocality sensitive hashing forest (e.g., the LSH forest generated inaccordance with FIG. 5 ) based at least in part on querying the localitysensitive hashing forest using one or more n-gram tokens generated fromthe input label. In some embodiments, the input label may be tokenizedaccording to the operations associated with the preprocessing stepsdiscussed at 208 of FIG. 2 and a set of n-gram tokens may be generatedfor each token of the input label. In some embodiments, the plurality ofcandidate labels identified from the LSH forest comprise a subset ofdomain labels identified from the plurality of domain labels.

At 710, a respective pairing for the input label and each candidatelabel of the plurality of candidate labels is generated. For example,the input label is paired with each candidate label to generate a numberof unique pairings.

At 712, a respective feature set is generated for each respectivepairing of the input label and each candidate label. In someembodiments, a respective feature set is generated based at least inpart on executing the operations discussed in connection with FIG. 4 .

At 714, each respective feature set is provided to the machine-learningmodel (e.g., the model 202) as input.

At 716, output from the machine-learning model is received. In someembodiments, the output identifies one or more of the candidate labelsas one or more recommended labels, the one or more recommended labelsbeing identified by the output as being similar to the input term. Adescription of the output is described in more detail above with respectto FIG. 6 .

At 718, the one or more recommendations corresponding to the one or morerecommended terms identified by the machine-learning model are provided(e.g., via the user device and/or user interface from which the userinput was received).

As noted above, infrastructure as a service (IaaS) is one particulartype of cloud computing. IaaS can be configured to provide virtualizedcomputing resources over a public network (e.g., the Internet). In anIaaS model, a cloud computing provider can host the infrastructurecomponents (e.g., servers, storage devices, network nodes (e.g.,hardware), deployment software, platform virtualization (e.g., ahypervisor layer), or the like). In some cases, an IaaS provider mayalso supply a variety of services to accompany those infrastructurecomponents (e.g., billing, monitoring, logging, load balancing andclustering, etc.). Thus, as these services may be policy-driven, IaaSusers may be able to implement policies to drive load balancing tomaintain application availability and performance.

In some instances, IaaS customers may access resources and servicesthrough a wide area network (WAN), such as the Internet, and can use thecloud provider's services to install the remaining elements of anapplication stack. For example, the user can log in to the IaaS platformto create virtual machines (VMs), install operating systems (OSs) oneach VM, deploy middleware such as databases, create storage buckets forworkloads and backups, and even install enterprise software into thatVM. Customers can then use the provider's services to perform variousfunctions, including balancing network traffic, troubleshootingapplication issues, monitoring performance, managing disaster recovery,etc.

In most cases, a cloud computing model will require the participation ofa cloud provider. The cloud provider may, but need not be, a third-partyservice that specializes in providing (e.g., offering, renting, selling)IaaS. An entity might also opt to deploy a private cloud, becoming itsown provider of infrastructure services.

In some examples, IaaS deployment is the process of putting a newapplication, or a new version of an application, onto a preparedapplication server or the like. It may also include the process ofpreparing the server (e.g., installing libraries, daemons, etc.). Thisis often managed by the cloud provider, below the hypervisor layer(e.g., the servers, storage, network hardware, and virtualization).Thus, the customer may be responsible for handling (OS), middleware,and/or application deployment (e.g., on self-service virtual machines(e.g., that can be spun up on demand) or the like.

In some examples, IaaS provisioning may refer to acquiring computers orvirtual hosts for use, and even installing needed libraries or serviceson them. In most cases, deployment does not include provisioning, andthe provisioning may need to be performed first.

In some cases, there are two different challenges for IaaS provisioning.First, there is the initial challenge of provisioning the initial set ofinfrastructure before anything is running. Second, there is thechallenge of evolving the existing infrastructure (e.g., adding newservices, changing services, removing services, etc.) once everythinghas been provisioned. In some cases, these two challenges may beaddressed by enabling the configuration of the infrastructure to bedefined declaratively. In other words, the infrastructure (e.g., whatcomponents are needed and how they interact) can be defined by one ormore configuration files. Thus, the overall topology of theinfrastructure (e.g., what resources depend on which, and how they eachwork together) can be described declaratively. In some instances, oncethe topology is defined, a workflow can be generated that creates and/ormanages the different components described in the configuration files.

In some examples, an infrastructure may have many interconnectedelements. For example, there may be one or more virtual private clouds(VPCs) (e.g., a potentially on-demand pool of configurable and/or sharedcomputing resources), also known as a core network. In some examples,there may also be one or more inbound/outbound traffic group rulesprovisioned to define how the inbound and/or outbound traffic of thenetwork will be set up and one or more virtual machines (VMs). Otherinfrastructure elements may also be provisioned, such as a loadbalancer, a database, or the like. As more and more infrastructureelements are desired and/or added, the infrastructure may incrementallyevolve.

In some instances, continuous deployment techniques may be employed toenable deployment of infrastructure code across various virtualcomputing environments. Additionally, the described techniques canenable infrastructure management within these environments. In someexamples, service teams can write code that is desired to be deployed toone or more, but often many, different production environments (e.g.,across various different geographic locations, sometimes spanning theentire world). However, in some examples, the infrastructure on whichthe code will be deployed must first be set up. In some instances, theprovisioning can be done manually, a provisioning tool may be utilizedto provision the resources, and/or deployment tools may be utilized todeploy the code once the infrastructure is provisioned.

FIG. 8 is a block diagram 800 illustrating an example pattern of an IaaSarchitecture, according to at least one embodiment. Service operators802 can be communicatively coupled to a secure host tenancy 804 that caninclude a virtual cloud network (VCN) 806 and a secure host subnet 808.In some examples, the service operators 802 may be using one or moreclient computing devices, which may be portable handheld devices (e.g.,an iPhone®, cellular telephone, an iPad®, computing tablet, a personaldigital assistant (PDA)) or wearable devices (e.g., a Google Glass® headmounted display), running software such as Microsoft Windows Mobile®,and/or a variety of mobile operating systems such as iOS, Windows Phone,Android, BlackBerry 8, Palm OS, and the like, and being Internet,e-mail, short message service (SMS), Blackberry®, or other communicationprotocol enabled. Alternatively, the client computing devices can begeneral purpose personal computers including, by way of example,personal computers, and/or laptop computers running various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems.The client computing devices can be workstation computers running any ofa variety of commercially-available UNIX® or UNIX-like operatingsystems, including without limitation the variety of GNU/Linux operatingsystems, such as for example, Google Chrome OS. Alternatively, or inaddition, client computing devices may be any other electronic device,such as a thin-client computer, an Internet-enabled gaming system (e.g.,a Microsoft Xbox gaming console with or without a Kinect® gesture inputdevice), and/or a personal messaging device, capable of communicatingover a network that can access the VCN 806 and/or the Internet.

The VCN 806 can include a local peering gateway (LPG) 810 that can becommunicatively coupled to a secure shell (SSH) VCN 812 via an LPG 810contained in the SSH VCN 812. The SSH VCN 812 can include an SSH subnet814, and the SSH VCN 812 can be communicatively coupled to a controlplane VCN 816 via the LPG 810 contained in the control plane VCN 816.Also, the SSH VCN 812 can be communicatively coupled to a data plane VCN818 via an LPG 810. The control plane VCN 816 and the data plane VCN 818can be contained in a service tenancy 819 that can be owned and/oroperated by the IaaS provider.

The control plane VCN 816 can include a control plane demilitarized zone(DMZ) tier 820 that acts as a perimeter network (e.g., portions of acorporate network between the corporate intranet and external networks).The DMZ-based servers may have restricted responsibilities and help keepbreaches contained. Additionally, the DMZ tier 820 can include one ormore load balancer (LB) subnet(s) 822, a control plane app tier 824 thatcan include app subnet(s) 826, a control plane data tier 828 that caninclude database (DB) subnet(s) 830 (e.g., frontend DB subnet(s) and/orbackend DB subnet(s)). The LB subnet(s) 822 contained in the controlplane DMZ tier 820 can be communicatively coupled to the app subnet(s)826 contained in the control plane app tier 824 and an Internet gateway834 that can be contained in the control plane VCN 816, and the appsubnet(s) 826 can be communicatively coupled to the DB subnet(s) 830contained in the control plane data tier 828 and a service gateway 836and a network address translation (NAT) gateway 838. The control planeVCN 816 can include the service gateway 836 and the NAT gateway 838.

The control plane VCN 816 can include a data plane mirror app tier 840that can include app subnet(s) 826. The app subnet(s) 826 contained inthe data plane mirror app tier 840 can include a virtual networkinterface controller (VNIC) 842 that can execute a compute instance 844.The compute instance 844 can communicatively couple the app subnet(s)826 of the data plane mirror app tier 840 to app subnet(s) 826 that canbe contained in a data plane app tier 846.

The data plane VCN 818 can include the data plane app tier 846, a dataplane DMZ tier 848, and a data plane data tier 850. The data plane DMZtier 848 can include LB subnet(s) 822 that can be communicativelycoupled to the app subnet(s) 826 of the data plane app tier 846 and theInternet gateway 834 of the data plane VCN 818. The app subnet(s) 826can be communicatively coupled to the service gateway 836 of the dataplane VCN 818 and the NAT gateway 838 of the data plane VCN 818. Thedata plane data tier 850 can also include the DB subnet(s) 830 that canbe communicatively coupled to the app subnet(s) 826 of the data planeapp tier 846.

The Internet gateway 834 of the control plane VCN 816 and of the dataplane VCN 818 can be communicatively coupled to a metadata managementservice 852 that can be communicatively coupled to public Internet 854.Public Internet 854 can be communicatively coupled to the NAT gateway838 of the control plane VCN 816 and of the data plane VCN 818. Theservice gateway 836 of the control plane VCN 816 and of the data planeVCN 818 can be communicatively couple to cloud services 856.

In some examples, the service gateway 836 of the control plane VCN 816or of the data plane VCN 818 can make application programming interface(API) calls to cloud services 856 without going through public Internet854. The API calls to cloud services 856 from the service gateway 836can be one-way: the service gateway 836 can make API calls to cloudservices 856, and cloud services 856 can send requested data to theservice gateway 836. But cloud services 856 may not initiate API callsto the service gateway 836.

In some examples, the secure host tenancy 804 can be directly connectedto the service tenancy 819, which may be otherwise isolated. The securehost subnet 808 can communicate with the SSH subnet 814 through an LPG810 that may enable two-way communication over an otherwise isolatedsystem. Connecting the secure host subnet 808 to the SSH subnet 814 maygive the secure host subnet 808 access to other entities within theservice tenancy 819.

The control plane VCN 816 may allow users of the service tenancy 819 toset up or otherwise provision desired resources. Desired resourcesprovisioned in the control plane VCN 816 may be deployed or otherwiseused in the data plane VCN 818. In some examples, the control plane VCN816 can be isolated from the data plane VCN 818, and the data planemirror app tier 840 of the control plane VCN 816 can communicate withthe data plane app tier 846 of the data plane VCN 818 via VNICs 842 thatcan be contained in the data plane mirror app tier 840 and the dataplane app tier 846.

In some examples, users of the system, or customers, can make requests,for example create, read, update, or delete (CRUD) operations, throughpublic Internet 854 that can communicate the requests to the metadatamanagement service 852. The metadata management service 852 cancommunicate the request to the control plane VCN 816 through theInternet gateway 834. The request can be received by the LB subnet(s)822 contained in the control plane DMZ tier 820. The LB subnet(s) 822may determine that the request is valid, and in response to thisdetermination, the LB subnet(s) 822 can transmit the request to appsubnet(s) 826 contained in the control plane app tier 824. If therequest is validated and requires a call to public Internet 854, thecall to public Internet 854 may be transmitted to the NAT gateway 838that can make the call to public Internet 854. Memory that may bedesired to be stored by the request can be stored in the DB subnet(s)830.

In some examples, the data plane mirror app tier 840 can facilitatedirect communication between the control plane VCN 816 and the dataplane VCN 818. For example, changes, updates, or other suitablemodifications to configuration may be desired to be applied to theresources contained in the data plane VCN 818. Via a VNIC 842, thecontrol plane VCN 816 can directly communicate with, and can therebyexecute the changes, updates, or other suitable modifications toconfiguration to, resources contained in the data plane VCN 818.

In some embodiments, the control plane VCN 816 and the data plane VCN818 can be contained in the service tenancy 819. In this case, the user,or the customer, of the system may not own or operate either the controlplane VCN 816 or the data plane VCN 818. Instead, the IaaS provider mayown or operate the control plane VCN 816 and the data plane VCN 818,both of which may be contained in the service tenancy 819. Thisembodiment can enable isolation of networks that may prevent users orcustomers from interacting with other users', or other customers',resources. Also, this embodiment may allow users or customers of thesystem to store databases privately without needing to rely on publicInternet 854, which may not have a desired level of threat prevention,for storage.

In other embodiments, the LB subnet(s) 822 contained in the controlplane VCN 816 can be configured to receive a signal from the servicegateway 836. In this embodiment, the control plane VCN 816 and the dataplane VCN 818 may be configured to be called by a customer of the IaaSprovider without calling public Internet 854. Customers of the IaaSprovider may desire this embodiment since database(s) that the customersuse may be controlled by the IaaS provider and may be stored on theservice tenancy 819, which may be isolated from public Internet 854.

FIG. 9 is a block diagram 900 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 902 (e.g., service operators 802 of FIG. 8 ) can becommunicatively coupled to a secure host tenancy 904 (e.g., the securehost tenancy 804 of FIG. 8 ) that can include a virtual cloud network(VCN) 906 (e.g., the VCN 806 of FIG. 8 ) and a secure host subnet 908(e.g., the secure host subnet 808 of FIG. 8 ). The VCN 906 can include alocal peering gateway (LPG) 910 (e.g., the LPG 810 of FIG. 8 ) that canbe communicatively coupled to a secure shell (SSH) VCN 912 (e.g., theSSH VCN 812 of FIG. 8 ) via an LPG 810 contained in the SSH VCN 912. TheSSH VCN 912 can include an SSH subnet 914 (e.g., the SSH subnet 814 ofFIG. 8 ), and the SSH VCN 912 can be communicatively coupled to acontrol plane VCN 916 (e.g., the control plane VCN 816 of FIG. 8 ) viaan LPG 910 contained in the control plane VCN 916. The control plane VCN916 can be contained in a service tenancy 919 (e.g., the service tenancy819 of FIG. 8 ), and the data plane VCN 918 (e.g., the data plane VCN818 of FIG. 8 ) can be contained in a customer tenancy 921 that may beowned or operated by users, or customers, of the system.

The control plane VCN 916 can include a control plane DMZ tier 920 (e.g.the control plane DMZ tier 820 of FIG. 8 ) that can include LB subnet(s)922 (e.g. LB subnet(s) 822 of FIG. 8 ), a control plane app tier 924(e.g. the control plane app tier 824 of FIG. 8 ) that can include appsubnet(s) 926 (e.g. app subnet(s) 826 of FIG. 8 ), a control plane datatier 928 (e.g. the control plane data tier 828 of FIG. 8 ) that caninclude database (DB) subnet(s) 930 (e.g. similar to DB subnet(s) 830 ofFIG. 8 ). The LB subnet(s) 922 contained in the control plane DMZ tier920 can be communicatively coupled to the app subnet(s) 926 contained inthe control plane app tier 924 and an Internet gateway 934 (e.g. theInternet gateway 834 of FIG. 8 ) that can be contained in the controlplane VCN 916, and the app subnet(s) 926 can be communicatively coupledto the DB subnet(s) 930 contained in the control plane data tier 928 anda service gateway 936 (e.g. the service gateway of FIG. 8 ) and anetwork address translation (NAT) gateway 938 (e.g. the NAT gateway 838of FIG. 8 ). The control plane VCN 916 can include the service gateway936 and the NAT gateway 938.

The control plane VCN 916 can include a data plane mirror app tier 940(e.g., the data plane mirror app tier 840 of FIG. 8 ) that can includeapp subnet(s) 926. The app subnet(s) 926 contained in the data planemirror app tier 940 can include a virtual network interface controller(VNIC) 942 (e.g., the VNIC of 842) that can execute a compute instance944 (e.g., similar to the compute instance 844 of FIG. 8 ). The computeinstance 944 can facilitate communication between the app subnet(s) 926of the data plane mirror app tier 940 and the app subnet(s) 926 that canbe contained in a data plane app tier 946 (e.g., the data plane app tier846 of FIG. 8 ) via the VNIC 942 contained in the data plane mirror apptier 940 and the VNIC 942 contained in the data plane app tier 946.

The Internet gateway 934 contained in the control plane VCN 916 can becommunicatively coupled to a metadata management service 952 (e.g., themetadata management service 852 of FIG. 8 ) that can be communicativelycoupled to public Internet 954 (e.g., public Internet 854 of FIG. 8 ).Public Internet 954 can be communicatively coupled to the NAT gateway938 contained in the control plane VCN 916. The service gateway 936contained in the control plane VCN 916 can be communicatively couple tocloud services 956 (e.g., cloud services 856 of FIG. 8 ).

In some examples, the data plane VCN 918 can be contained in thecustomer tenancy 921. In this case, the IaaS provider may provide thecontrol plane VCN 916 for each customer, and the IaaS provider may, foreach customer, set up a unique compute instance 944 that is contained inthe service tenancy 919. Each compute instance 944 may allowcommunication between the control plane VCN 916, contained in theservice tenancy 919, and the data plane VCN 918 that is contained in thecustomer tenancy 921. The compute instance 944 may allow resources, thatare provisioned in the control plane VCN 916 that is contained in theservice tenancy 919, to be deployed or otherwise used in the data planeVCN 918 that is contained in the customer tenancy 921.

In other examples, the customer of the IaaS provider may have databasesthat live in the customer tenancy 921. In this example, the controlplane VCN 916 can include the data plane mirror app tier 940 that caninclude app subnet(s) 926. The data plane mirror app tier 940 can residein the data plane VCN 918, but the data plane mirror app tier 940 maynot live in the data plane VCN 918. That is, the data plane mirror apptier 940 may have access to the customer tenancy 921, but the data planemirror app tier 940 may not exist in the data plane VCN 918 or be ownedor operated by the customer of the IaaS provider. The data plane mirrorapp tier 940 may be configured to make calls to the data plane VCN 918but may not be configured to make calls to any entity contained in thecontrol plane VCN 916. The customer may desire to deploy or otherwiseuse resources in the data plane VCN 918 that are provisioned in thecontrol plane VCN 916, and the data plane mirror app tier 940 canfacilitate the desired deployment, or other usage of resources, of thecustomer.

In some embodiments, the customer of the IaaS provider can apply filtersto the data plane VCN 918. In this embodiment, the customer candetermine what the data plane VCN 918 can access, and the customer mayrestrict access to public Internet 954 from the data plane VCN 918. TheIaaS provider may not be able to apply filters or otherwise controlaccess of the data plane VCN 918 to any outside networks or databases.Applying filters and controls by the customer onto the data plane VCN918, contained in the customer tenancy 921, can help isolate the dataplane VCN 918 from other customers and from public Internet 954.

In some embodiments, cloud services 956 can be called by the servicegateway 936 to access services that may not exist on public Internet954, on the control plane VCN 916, or on the data plane VCN 918. Theconnection between cloud services 956 and the control plane VCN 916 orthe data plane VCN 918 may not be live or continuous. Cloud services 956may exist on a different network owned or operated by the IaaS provider.Cloud services 956 may be configured to receive calls from the servicegateway 936 and may be configured to not receive calls from publicInternet 954. Some cloud services 956 may be isolated from other cloudservices 956, and the control plane VCN 916 may be isolated from cloudservices 956 that may not be in the same region as the control plane VCN916. For example, the control plane VCN 916 may be located in “Region1,” and cloud service “Deployment 8,” may be located in Region 1 and in“Region 2.” If a call to Deployment 8 is made by the service gateway 936contained in the control plane VCN 916 located in Region 1, the call maybe transmitted to Deployment 8 in Region 1. In this example, the controlplane VCN 916, or Deployment 8 in Region 1, may not be communicativelycoupled to, or otherwise in communication with, Deployment 6 in Region2.

FIG. 10 is a block diagram 1000 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1002 (e.g., service operators 802 of FIG. 8 ) can becommunicatively coupled to a secure host tenancy 1004 (e.g., the securehost tenancy 804 of FIG. 8 ) that can include a virtual cloud network(VCN) 1006 (e.g., the VCN 806 of FIG. 8 ) and a secure host subnet 1008(e.g., the secure host subnet 808 of FIG. 8 ). The VCN 1006 can includean LPG 1010 (e.g. the LPG 810 of FIG. 8 ) that can be communicativelycoupled to an SSH VCN 1012 (e.g. the SSH VCN 812 of FIG. 8 ) via an LPG1010 contained in the SSH VCN 1012. The SSH VCN 1012 can include an SSHsubnet 1014 (e.g. the SSH subnet 814 of FIG. 8 ), and the SSH VCN 1012can be communicatively coupled to a control plane VCN 1016 (e.g. thecontrol plane VCN 816 of FIG. 8 ) via an LPG 1010 contained in thecontrol plane VCN 1016 and to a data plane VCN 1018 (e.g. the data plane818 of FIG. 8 ) via an LPG 1010 contained in the data plane VCN 1018.The control plane VCN 1016 and the data plane VCN 1018 can be containedin a service tenancy 1019 (e.g. the service tenancy 819 of FIG. 8 ).

The control plane VCN 1016 can include a control plane DMZ tier 1020(e.g. the control plane DMZ tier 820 of FIG. 8 ) that can include loadbalancer (LB) subnet(s) 1022 (e.g. LB subnet(s) 822 of FIG. 8 ), acontrol plane app tier 1024 (e.g. the control plane app tier 824 of FIG.8 ) that can include app subnet(s) 1026 (e.g. similar to app subnet(s)826 of FIG. 8 ), a control plane data tier 1028 (e.g. the control planedata tier 828 of FIG. 8 ) that can include DB subnet(s) 1030. The LBsubnet(s) 1022 contained in the control plane DMZ tier 1020 can becommunicatively coupled to the app subnet(s) 1026 contained in thecontrol plane app tier 1024 and to an Internet gateway 1034 (e.g. theInternet gateway 834 of FIG. 8 ) that can be contained in the controlplane VCN 1016, and the app subnet(s) 1026 can be communicativelycoupled to the DB subnet(s) 1030 contained in the control plane datatier 1028 and to a service gateway 1036 (e.g. the service gateway ofFIG. 8 ) and a network address translation (NAT) gateway 1038 (e.g. theNAT gateway 838 of FIG. 8 ). The control plane VCN 1016 can include theservice gateway 1036 and the NAT gateway 1038.

The data plane VCN 1018 can include a data plane app tier 1046 (e.g. thedata plane app tier 846 of FIG. 8 ), a data plane DMZ tier 1048 (e.g.the data plane DMZ tier 848 of FIG. 8 ), and a data plane data tier 1050(e.g. the data plane data tier 850 of FIG. 8 ). The data plane DMZ tier1048 can include LB subnet(s) 1022 that can be communicatively coupledto trusted app subnet(s) 1060 and untrusted app subnet(s) 1062 of thedata plane app tier 1046 and the Internet gateway 1034 contained in thedata plane VCN 1018. The trusted app subnet(s) 1060 can becommunicatively coupled to the service gateway 1036 contained in thedata plane VCN 1018, the NAT gateway 1038 contained in the data planeVCN 1018, and DB subnet(s) 1030 contained in the data plane data tier1050. The untrusted app subnet(s) 1062 can be communicatively coupled tothe service gateway 1036 contained in the data plane VCN 1018 and DBsubnet(s) 1030 contained in the data plane data tier 1050. The dataplane data tier 1050 can include DB subnet(s) 1030 that can becommunicatively coupled to the service gateway 1036 contained in thedata plane VCN 1018.

The untrusted app subnet(s) 1062 can include one or more primary VNICs1064(1)-(N) that can be communicatively coupled to tenant virtualmachines (VMs) 1066(1)-(N). Each tenant VM 1066(1)-(N) can becommunicatively coupled to a respective app subnet 1067(1)-(N) that canbe contained in respective container egress VCNs 1068(1)-(N) that can becontained in respective customer tenancies 1070(1)-(N). Respectivesecondary VNICs 1072(1)-(N) can facilitate communication between theuntrusted app subnet(s) 1062 contained in the data plane VCN 1018 andthe app subnet contained in the container egress VCNs 1068(1)-(N). Eachcontainer egress VCNs 1068(1)-(N) can include a NAT gateway 1038 thatcan be communicatively coupled to public Internet 1054 (e.g. publicInternet 854 of FIG. 8 ).

The Internet gateway 1034 contained in the control plane VCN 1016 andcontained in the data plane VCN 1018 can be communicatively coupled to ametadata management service 1052 (e.g. the metadata management system852 of FIG. 8 ) that can be communicatively coupled to public Internet1054. Public Internet 1054 can be communicatively coupled to the NATgateway 1038 contained in the control plane VCN 1016 and contained inthe data plane VCN 1018. The service gateway 1036 contained in thecontrol plane VCN 1016 and contained in the data plane VCN 1018 can becommunicatively couple to cloud services 1056.

In some embodiments, the data plane VCN 1018 can be integrated withcustomer tenancies 1070. This integration can be useful or desirable forcustomers of the IaaS provider in some cases such as a case that maydesire support when executing code. The customer may provide code to runthat may be destructive, may communicate with other customer resources,or may otherwise cause undesirable effects. In response to this, theIaaS provider may determine whether to run code given to the IaaSprovider by the customer.

In some examples, the customer of the IaaS provider may grant temporarynetwork access to the IaaS provider and request a function to beattached to the data plane tier app 1046. Code to run the function maybe executed in the VMs 1066(1)-(N), and the code may not be configuredto run anywhere else on the data plane VCN 1018. Each VM 1066(1)-(N) maybe connected to one customer tenancy 1070. Respective containers1071(1)-(N) contained in the VMs 1066(1)-(N) may be configured to runthe code. In this case, there can be a dual isolation (e.g., thecontainers 1071(1)-(N) running code, where the containers 1071(1)-(N)may be contained in at least the VM 1066(1)-(N) that are contained inthe untrusted app subnet(s) 1062), which may help prevent incorrect orotherwise undesirable code from damaging the network of the IaaSprovider or from damaging a network of a different customer. Thecontainers 1071(1)-(N) may be communicatively coupled to the customertenancy 1070 and may be configured to transmit or receive data from thecustomer tenancy 1070. The containers 1071(1)-(N) may not be configuredto transmit or receive data from any other entity in the data plane VCN1018. Upon completion of running the code, the IaaS provider may kill orotherwise dispose of the containers 1071(1)-(N).

In some embodiments, the trusted app subnet(s) 1060 may run code thatmay be owned or operated by the IaaS provider. In this embodiment, thetrusted app subnet(s) 1060 may be communicatively coupled to the DBsubnet(s) 1030 and be configured to execute CRUD operations in the DBsubnet(s) 1030. The untrusted app subnet(s) 1062 may be communicativelycoupled to the DB subnet(s) 1030, but in this embodiment, the untrustedapp subnet(s) may be configured to execute read operations in the DBsubnet(s) 1030. The containers 1071(1)-(N) that can be contained in theVM 1066(1)-(N) of each customer and that may run code from the customermay not be communicatively coupled with the DB subnet(s) 1030.

In other embodiments, the control plane VCN 1016 and the data plane VCN1018 may not be directly communicatively coupled. In this embodiment,there may be no direct communication between the control plane VCN 1016and the data plane VCN 1018. However, communication can occur indirectlythrough at least one method. An LPG 1010 may be established by the IaaSprovider that can facilitate communication between the control plane VCN1016 and the data plane VCN 1018. In another example, the control planeVCN 1016 or the data plane VCN 1018 can make a call to cloud services1056 via the service gateway 1036. For example, a call to cloud services1056 from the control plane VCN 1016 can include a request for a servicethat can communicate with the data plane VCN 1018.

FIG. 11 is a block diagram 1100 illustrating another example pattern ofan IaaS architecture, according to at least one embodiment. Serviceoperators 1102 (e.g. service operators 802 of FIG. 8 ) can becommunicatively coupled to a secure host tenancy 1104 (e.g. the securehost tenancy 804 of FIG. 8 ) that can include a virtual cloud network(VCN) 1106 (e.g. the VCN 806 of FIG. 8 ) and a secure host subnet 1108(e.g. the secure host subnet 808 of FIG. 8 ). The VCN 1106 can includean LPG 1110 (e.g. the LPG 810 of FIG. 8 ) that can be communicativelycoupled to an SSH VCN 1112 (e.g. the SSH VCN 812 of FIG. 8 ) via an LPG1110 contained in the SSH VCN 1112. The SSH VCN 1112 can include an SSHsubnet 1114 (e.g. the SSH subnet 814 of FIG. 8 ), and the SSH VCN 1112can be communicatively coupled to a control plane VCN 1116 (e.g. thecontrol plane VCN 816 of FIG. 8 ) via an LPG 1110 contained in thecontrol plane VCN 1116 and to a data plane VCN 1118 (e.g. the data plane818 of FIG. 8 ) via an LPG 1110 contained in the data plane VCN 1118.The control plane VCN 1116 and the data plane VCN 1118 can be containedin a service tenancy 1119 (e.g. the service tenancy 819 of FIG. 8 ).

The control plane VCN 1116 can include a control plane DMZ tier 1120(e.g. the control plane DMZ tier 820 of FIG. 8 ) that can include LBsubnet(s) 1122 (e.g. LB subnet(s) 822 of FIG. 8 ), a control plane apptier 1124 (e.g. the control plane app tier 824 of FIG. 8 ) that caninclude app subnet(s) 1126 (e.g. app subnet(s) 826 of FIG. 8 ), acontrol plane data tier 1128 (e.g. the control plane data tier 828 ofFIG. 8 ) that can include DB subnet(s) 1130 (e.g. DB subnet(s) 1030 ofFIG. 10 ). The LB subnet(s) 1122 contained in the control plane DMZ tier1120 can be communicatively coupled to the app subnet(s) 1126 containedin the control plane app tier 1124 and to an Internet gateway 1134 (e.g.the Internet gateway 834 of FIG. 8 ) that can be contained in thecontrol plane VCN 1116, and the app subnet(s) 1126 can becommunicatively coupled to the DB subnet(s) 1130 contained in thecontrol plane data tier 1128 and to a service gateway 1136 (e.g. theservice gateway of FIG. 8 ) and a network address translation (NAT)gateway 1138 (e.g. the NAT gateway 838 of FIG. 8 ). The control planeVCN 1116 can include the service gateway 1136 and the NAT gateway 1138.

The data plane VCN 1118 can include a data plane app tier 1146 (e.g. thedata plane app tier 846 of FIG. 8 ), a data plane DMZ tier 1148 (e.g.the data plane DMZ tier 848 of FIG. 8 ), and a data plane data tier 1150(e.g. the data plane data tier 850 of FIG. 8 ). The data plane DMZ tier1148 can include LB subnet(s) 1122 that can be communicatively coupledto trusted app subnet(s) 1160 (e.g. trusted app subnet(s) 1060 of FIG.10 ) and untrusted app subnet(s) 1162 (e.g. untrusted app subnet(s) 1062of FIG. 10 ) of the data plane app tier 1146 and the Internet gateway1134 contained in the data plane VCN 1118. The trusted app subnet(s)1160 can be communicatively coupled to the service gateway 1136contained in the data plane VCN 1118, the NAT gateway 1138 contained inthe data plane VCN 1118, and DB subnet(s) 1130 contained in the dataplane data tier 1150. The untrusted app subnet(s) 1162 can becommunicatively coupled to the service gateway 1136 contained in thedata plane VCN 1118 and DB subnet(s) 1130 contained in the data planedata tier 1150. The data plane data tier 1150 can include DB subnet(s)1130 that can be communicatively coupled to the service gateway 1136contained in the data plane VCN 1118.

The untrusted app subnet(s) 1162 can include primary VNICs 1164(1)-(N)that can be communicatively coupled to tenant virtual machines (VMs)1166(1)-(N) residing within the untrusted app subnet(s) 1162. Eachtenant VM 1166(1)-(N) can run code in a respective container1167(1)-(N), and be communicatively coupled to an app subnet 1126 thatcan be contained in a data plane app tier 1146 that can be contained ina container egress VCN 1168. Respective secondary VNICs 1172(1)-(N) canfacilitate communication between the untrusted app subnet(s) 1162contained in the data plane VCN 1118 and the app subnet contained in thecontainer egress VCN 1168. The container egress VCN can include a NATgateway 1138 that can be communicatively coupled to public Internet 1154(e.g. public Internet 854 of FIG. 8 ).

The Internet gateway 1134 contained in the control plane VCN 1116 andcontained in the data plane VCN 1118 can be communicatively coupled to ametadata management service 1152 (e.g. the metadata management system852 of FIG. 8 ) that can be communicatively coupled to public Internet1154. Public Internet 1154 can be communicatively coupled to the NATgateway 1138 contained in the control plane VCN 1116 and contained inthe data plane VCN 1118. The service gateway 1136 contained in thecontrol plane VCN 1116 and contained in the data plane VCN 1118 can becommunicatively couple to cloud services 1156.

In some examples, the pattern illustrated by the architecture of blockdiagram 1100 of FIG. 11 may be considered an exception to the patternillustrated by the architecture of block diagram 1000 of FIG. 10 and maybe desirable for a customer of the IaaS provider if the IaaS providercannot directly communicate with the customer (e.g., a disconnectedregion). The respective containers 1167(1)-(N) that are contained in theVMs 1166(1)-(N) for each customer can be accessed in real-time by thecustomer. The containers 1167(1)-(N) may be configured to make calls torespective secondary VNICs 1172(1)-(N) contained in app subnet(s) 1126of the data plane app tier 1146 that can be contained in the containeregress VCN 1168. The secondary VNICs 1172(1)-(N) can transmit the callsto the NAT gateway 1138 that may transmit the calls to public Internet1154. In this example, the containers 1167(1)-(N) that can be accessedin real-time by the customer can be isolated from the control plane VCN1116 and can be isolated from other entities contained in the data planeVCN 1118. The containers 1167(1)-(N) may also be isolated from resourcesfrom other customers.

In other examples, the customer can use the containers 1167(1)-(N) tocall cloud services 1156. In this example, the customer may run code inthe containers 1167(1)-(N) that requests a service from cloud services1156. The containers 1167(1)-(N) can transmit this request to thesecondary VNICs 1172(1)-(N) that can transmit the request to the NATgateway that can transmit the request to public Internet 1154. PublicInternet 1154 can transmit the request to LB subnet(s) 1122 contained inthe control plane VCN 1116 via the Internet gateway 1134. In response todetermining the request is valid, the LB subnet(s) can transmit therequest to app subnet(s) 1126 that can transmit the request to cloudservices 1156 via the service gateway 1136.

It should be appreciated that IaaS architectures 800, 900, 1000, 1100depicted in the figures may have other components than those depicted.Further, the embodiments shown in the figures are only some examples ofa cloud infrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, the IaaS systems may have more orfewer components than shown in the figures, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

In certain embodiments, the IaaS systems described herein may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such an IaaS system is the Oracle Cloud Infrastructure (OCI)provided by the present assignee.

FIG. 12 illustrates an example computer system 1200, in which variousembodiments may be implemented. The system 1200 may be used to implementany of the computer systems described above. As shown in the figure,computer system 1200 includes a processing unit 1204 that communicateswith a number of peripheral subsystems via a bus subsystem 1202. Theseperipheral subsystems may include a processing acceleration unit 1206,an I/O subsystem 1208, a storage subsystem 1218 and a communicationssubsystem 1224. Storage subsystem 1218 includes tangiblecomputer-readable storage media 1222 and a system memory 1210.

Bus subsystem 1202 provides a mechanism for letting the variouscomponents and subsystems of computer system 1200 communicate with eachother as intended. Although bus subsystem 1202 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1202 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 1204, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 1200. One or more processorsmay be included in processing unit 1204. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 1204 may be implemented as one or more independent processing units1232 and/or 1234 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 1204 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 1204 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some,or all of the program code to be executed can be resident inprocessor(s) 1204 and/or in storage subsystem 1218. Through suitableprogramming, processor(s) 1204 can provide various functionalitiesdescribed above. Computer system 1200 may additionally include aprocessing acceleration unit 1206, which can include a digital signalprocessor (DSP), a special-purpose processor, and/or the like.

I/O subsystem 1208 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments, and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1200 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics, and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 1200 may comprise a storage subsystem 1218 thatcomprises software elements, shown as being currently located within asystem memory 1210. System memory 1210 may store program instructionsthat are loadable and executable on processing unit 1204, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 1200, systemmemory 1210 may be volatile (such as random-access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 1204. In some implementations, system memory 1210 may includemultiple different types of memory, such as static random-access memory(SRAM) or dynamic random-access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system1200, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 1210 also illustratesapplication programs 1212, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 1214, and an operating system 1216. By wayof example, operating system 1216 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 12 OS, andPalm® OS operating systems.

Storage subsystem 1218 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem1218. These software modules or instructions may be executed byprocessing unit 1204. Storage subsystem 1218 may also provide arepository for storing data used in accordance with the presentdisclosure.

Storage subsystem 1200 may also include a computer-readable storagemedia reader 1220 that can further be connected to computer-readablestorage media 1222. Together and, optionally, in combination with systemmemory 1210, computer-readable storage media 1222 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 1222 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 1200.

By way of example, computer-readable storage media 1222 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 1222 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1222 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 1200.

Communications subsystem 1224 provides an interface to other computersystems and networks. Communications subsystem 1224 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1200. For example, communications subsystem 1224may enable computer system 1200 to connect to one or more devices viathe Internet. In some embodiments communications subsystem 1224 caninclude radio frequency (RF) transceiver components for accessingwireless voice and/or data networks (e.g., using cellular telephonetechnology, advanced data network technology, such as 3G, 4G or EDGE(enhanced data rates for global evolution), WiFi (IEEE 802.11 familystandards, or other mobile communication technologies, or anycombination thereof), global positioning system (GPS) receivercomponents, and/or other components. In some embodiments communicationssubsystem 1224 can provide wired network connectivity (e.g., Ethernet)in addition to or instead of a wireless interface.

In some embodiments, communications subsystem 1224 may also receiveinput communication in the form of structured and/or unstructured datafeeds 1226, event streams 1228, event updates 1230, and the like onbehalf of one or more users who may use computer system 1200.

By way of example, communications subsystem 1224 may be configured toreceive data feeds 1226 in real-time from users of social networksand/or other communication services such as Twitter® feeds, Facebook®updates, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources.

Additionally, communications subsystem 1224 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 1228 of real-time events and/or event updates 1230, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1224 may also be configured to output thestructured and/or unstructured data feeds 1226, event streams 1228,event updates 1230, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1200.

Computer system 1200 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 1200 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

Although specific embodiments have been described, variousmodifications, alterations, alternative constructions, and equivalentsare also encompassed within the scope of the disclosure. Embodiments arenot restricted to operation within certain specific data processingenvironments but are free to operate within a plurality of dataprocessing environments. Additionally, although embodiments have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentdisclosure is not limited to the described series of transactions andsteps. Various features and aspects of the above-described embodimentsmay be used individually or jointly.

Further, while embodiments have been described using a particularcombination of hardware and software, it should be recognized that othercombinations of hardware and software are also within the scope of thepresent disclosure. Embodiments may be implemented only in hardware, oronly in software, or using combinations thereof. The various processesdescribed herein can be implemented on the same processor or differentprocessors in any combination. Accordingly, where components or modulesare described as being configured to perform certain operations, suchconfiguration can be accomplished, e.g., by designing electroniccircuits to perform the operation, by programming programmableelectronic circuits (such as microprocessors) to perform the operation,or any combination thereof. Processes can communicate using a variety oftechniques including but not limited to conventional techniques forinter process communication, and different pairs of processes may usedifferent techniques, or the same pair of processes may use differenttechniques at different times.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificdisclosure embodiments have been described, these are not intended to belimiting. Various modifications and equivalents are within the scope ofthe following claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected” is to be construed as partly or wholly contained within,attached to, or joined together, even if there is something intervening.Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate embodiments and does not pose alimitation on the scope of the disclosure unless otherwise claimed. Nolanguage in the specification should be construed as indicating anynon-claimed element as essential to the practice of the disclosure.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is intended to be understoodwithin the context as used in general to present that an item, term,etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y,and/or Z). Thus, such disjunctive language is not generally intended to,and should not, imply that certain embodiments require at least one ofX, at least one of Y, or at least one of Z to each be present.

Preferred embodiments of this disclosure are described herein, includingthe best mode known for carrying out the disclosure. Variations of thosepreferred embodiments may become apparent to those of ordinary skill inthe art upon reading the foregoing description. Those of ordinary skillshould be able to employ such variations as appropriate and thedisclosure may be practiced otherwise than as specifically describedherein. Accordingly, this disclosure includes all modifications andequivalents of the subject matter recited in the claims appended heretoas permitted by applicable law. Moreover, any combination of theabove-described elements in all possible variations thereof isencompassed by the disclosure unless otherwise indicated herein.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

What is claimed is:
 1. A computer-implemented method, comprising:obtaining, by a computing device, a machine-learning model that isconfigured to identify a similarity between a first label and a secondlabel provided as input, the machine-learning model being previouslytrained based at least in part on a supervised learning algorithm and atraining data set including pairs of labels that have been previouslyidentified as being valid or invalid pairs, the first labelcorresponding to technical metadata and the second label correspondingto a domain term; generating a locality sensitive hashing (LSH) forestbased at least in part on one or more respective n-gram tokens generatedfrom respective domain labels of a plurality of domain labels;receiving, from a user interface, user input identifying an input label;identifying a plurality of candidate labels from the LSH forest based atleast in part on querying the LSH forest using one or more n-gram tokensgenerated from the input label, the plurality of candidate labelscomprising a subset of domain labels from the plurality of domainlabels; generating a respective pairing for the input label and eachcandidate label of the plurality of candidate labels; generating arespective feature set for each respective pairing of the input labeland each candidate label; providing each respective feature set to themachine-learning model as input; receiving output from themachine-learning model identifying one or more of the candidate labelsas one or more recommended labels, the one or more recommended labelsbeing identified by the output as being similar to the input label; andproviding, via the user interface, a recommendation corresponding to arecommended label of the one or more recommended labels identified bythe machine-learning model.
 2. The computer-implemented method of claim1, wherein the respective feature set for a respective pairing comprisesa plurality of similarity scores generated by providing each respectivepairing as input to a plurality of similarity algorithms, each of theplurality of similarity algorithms being configured to assign thepairing a score indicating a degree of similarity between labels of eachrespective pairing.
 3. The computer-implemented method of claim 1,wherein generating the LSH comprises: generating respective sets ofn-gram tokens for each of the domain labels of the plurality of domainlabels; computing a plurality of minimum hash values for each n-gramtoken of each domain labels of the plurality of domain labels based atleast in part on the respective sets of n-gram tokens and a hashingfunction; and generating a plurality of tree structures for the LSHforest based at least in part on the plurality of minimum hash values.4. The computer-implemented method of claim 1, further comprising:generating, automatically or in response to subsequent user input, anassociation between the input label and the recommended label; andstoring the association between the input label and the recommendedlabel for subsequent use.
 5. The computer-implemented method of claim 4,further comprising providing the association between the input label andthe recommended label as additional training data with which themachine-learning model is updated.
 6. The computer-implemented method ofclaim 4, further comprising: receiving a search query comprising one ormore search terms; and obtaining a set of search results based at leastin part on executing a query with the one or more search terms, the oneor more search terms comprising at least the recommended label, therecommended label being obtained as a search result of the set of searchresults based at least in part on the association between the inputlabel and the recommended label.
 7. The computer-implemented method ofclaim 4, wherein the recommended label is associated with a data entitycorresponding an attribute or column of a database.
 8. A data managementservice, comprising: one or more processors; and one or more memoriesstoring computer-executable instructions that, when executed with theone or more processors, cause the data management service to: obtain amachine-learning model that is configured to identify a similaritybetween a first label and a second label provided as input, themachine-learning model being previously trained based at least in parton a supervised learning algorithm and a training data set includingpairs of labels that have been previously identified as being valid orinvalid pairs, the first label corresponding to technical metadata andthe second label corresponding to a domain term; generate a (LSH) forestbased at least in part on one or more respective n-gram tokens generatedfrom respective domain labels of a plurality of domain labels; receive,at a user interface, user input identifying an input label; identify aplurality of candidate labels from the LSH forest based at least in parton querying the LSH forest using one or more n-gram tokens generatedfrom the input label, the plurality of candidate labels comprising asubset of domain labels from the plurality of domain labels; generate arespective pairing for the input label and each candidate label of theplurality of candidate labels; generate a respective feature set foreach respective pairing of the input label and each candidate label;provide each respective feature set to the machine-learning model asinput; receive output from the machine-learning model, the outputidentifying one or more of the candidate labels as one or morerecommended labels, the one or more recommended labels being identifiedby the output as being similar to the input label; and provide, at theuser interface, a recommendation corresponding to a recommendation labelof the one or more recommended labels identified by the machine-learningmodel.
 9. The data management service of claim 8, wherein the respectivefeature set for a respective pairing comprises a plurality of similarityscores generated by providing each respective pairing as input to aplurality of similarity algorithms, each of the plurality of similarityalgorithms being configured to assign the pairing a score indicating adegree of similarity between labels of each respective pairing.
 10. Thedata management service of claim 8, wherein generating the LSH forestcauses the data management service to: generate respective sets ofn-gram tokens for each of the domain labels of the plurality of domainlabels; compute a plurality of minimum hash values for each n-gram tokenof each domain labels of the plurality of domain labels based at leastin part on the respective sets of n-gram tokens and a hashing function;and generate a plurality of tree structures for the LSH forest based atleast in part on the plurality of minimum hash values.
 11. The datamanagement service of claim 8, wherein executing the computer-executableinstructions further causes the data management service to: generate,automatically or in response to subsequent user input, an associationbetween the input label and the recommended label; and store theassociation between the input label and the recommended label forsubsequent use.
 12. The data management service of claim 11, whereinexecuting the computer-executable instructions further causes the datamanagement service to provide the association between the input labeland the recommended label as additional training data with which themachine-learning model is updated.
 13. The data management service ofclaim 11, wherein executing the computer-executable instructions furthercauses the data management service to: receive a search query comprisingone or more search terms; and obtain a set of search results based atleast in part on executing a query with the one or more search terms,the one or more search terms comprising at least the recommended label,the recommended label being obtained as a search result of the set ofsearch results based at least in part on the association between theinput label and the recommended label.
 14. The data management serviceof claim 11, wherein the recommended label is associated with atechnical asset corresponding an attribute or column of a database. 15.A non-transitory computer-readable storage medium comprising executableinstructions that, when executed with one or more processors of acomputing device, cause the computing device to: obtain amachine-learning model that is configured to identify a similaritybetween a first label and a second label provided as input, themachine-learning model being previously trained based at least in parton a supervised learning algorithm and a training data set includingpairs of labels that have been previously identified as being valid orinvalid pairs, the first label corresponding to technical metadata andthe second label corresponding to a domain term; generate a (LSH) forestbased at least in part on one or more respective n-gram tokens generatedfrom respective domain labels of a plurality of domain labels; receive,at a user interface, user input identifying an input label; identify aplurality of candidate labels from the LSH forest based at least in parton querying the LSH forest using one or more n-gram tokens generatedfrom the input label, the plurality of candidate labels comprising asubset of domain labels from the plurality of domain labels; generate arespective pairing for the input label and each candidate label of theplurality of candidate labels; generate a respective feature set foreach respective pairing of the input label and each candidate label;provide each respective feature set to the machine-learning model asinput; receive output from the machine-learning model, the outputidentifying one or more of the candidate labels as one or morerecommended labels, the one or more recommended labels being identifiedby the output as being similar to the input label; and provide, to theuser interface, a recommendation corresponding to a recommended label ofthe one or more recommended labels identified by the machine-learningmodel.
 16. The non-transitory computer-readable storage medium of claim15, wherein the respective feature set for a respective pairingcomprises a plurality of similarity scores generated by providing eachrespective pairing as input to a plurality of similarity algorithms,each of the plurality of similarity algorithms being configured toassign the pairing a score indicating a degree of similarity betweenlabels of each respective pairing.
 17. The non-transitorycomputer-readable storage medium claim 15, wherein generating the LSHforest causes the computing device to: generate respective sets ofn-gram tokens for each of the domain labels of the plurality of domainlabels; compute a plurality of minimum hash values for each n-gram tokenof each domain labels of the plurality of domain labels based at leastin part on the respective sets of n-gram tokens and a hashing function;and generate a plurality of tree structures for the LSH forest based atleast in part on the plurality of minimum hash values.
 18. Thenon-transitory computer-readable storage medium of claim 15, whereinexecuting the executable instructions further causes the computingdevice to: generate, automatically or in response to subsequent userinput, an association between the input label and the recommended label;and store the association between the input label and the recommendedlabel for subsequent use.
 19. The non-transitory computer-readablestorage medium of claim 18, wherein executing the executableinstructions further causes the computing device to provide theassociation between the input label and the recommended label asadditional training data with which the machine-learning model isupdated.
 20. The non-transitory computer-readable storage medium ofclaim 18, wherein executing the executable instructions further causesthe computing device to: receive a search query comprising one or moresearch terms; and obtain a set of search results based at least in parton executing a query with the one or more search terms, the one or moresearch terms comprising at least the recommended label, the recommendedlabel being obtained as a search result of the set of search resultsbased at least in part on the association between the input label andthe recommended label.